Machine Learning Introduction | Machine Learning Python | (Data Science ss : 10)

Machine Learning Introduction

*Whats is Machine Learning?

It is an application of AI that provides system that ability to automatically learn & improve from experience without being explicitly programmed.

* Terminology :

(i) Dataset :

    Set of Data in form of list or an ordered collection which feature important to solve a problem.

(ii) Feature :

      Important pieces of information in the data that helps us understand a problem.

(iii) Model :

      Denotes phenomenon that Machine Learning Algorithms has learned & it denotes the output after the process of training.

* Process :

Data Collection : Collect the data that the Algorithm will actually learn from.

Data Preparation : Format & engineer the data into the optimal format.

Training : Process where the Machine Learning actually learn.

Evaluation : Test the model to see how well it perform at all times.

Tuning : Fine tune the model to maximize the performance.

*What are Algorithms?

Algorithms are process or set of rules to be  followed in calculations or other problem-solving operations, especially by a computer.

*Types of Machine Learning Algorithms :

Linear Regression

Logistic Regression

Decision Tree

SVM

Naïve Bayes

KNN

K-Means

Random Forest

Dimensionality Reduction Algorithm

Gradient Descent

We will see every Algorithm one by one in detail. Before diving into this let’s clarify some basic concepts first.

*Difference Between Artificial Intelligence , Machine Learning & Deep Learning.

Mentioned above is the picture which shows the key difference between the 3 concepts.